Significant developments in colorectal cancer screening are underway and include new screening guidelines that incorporate considerations for patients aged 45 years, with unique features and new techniques at the forefront of screening. One of these new techniques is artificial intelligence which can increase adenoma detection rate and reduce the prevalence of colonic neoplasia.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260867 | PMC |
| http://dx.doi.org/10.3748/wjg.v28.i24.2778 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.
J Hand Surg Eur Vol
January 2025
Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.
View Article and Find Full Text PDFMicroRNA Expression in High-Grade B-Cell Lymphoma With 11q Aberration.
Genes Chromosomes Cancer
January 2025
Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany.
Mature aggressive B-cell lymphomas, such as Burkitt lymphoma (BL) and Diffuse large B-cell lymphoma (DLBCL), show variations in microRNA (miRNA) expression. The entity of High-grade B-cell lymphoma with 11q aberration (HGBCL-11q) shares several biological features with both BL and DLBCL but data on its miRNA expression profile are yet scarce. Hence, this study aims to analyze the potential differences in miRNA expression of HGBCL-11q compared to BL and DLBCL.
View Article and Find Full Text PDFArtificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.
Clin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Performance of computerized self-reported medical history taking and HEAR score for safe early rule-out of cardiac events in acute chest pain patients: the CLEOS-CPDS prospective cohort study.
Eur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
View Article and Find Full Text PDFPerformance of Chat Generative Pre-trained Transformer-4o in the Adult Clinical Cardiology Self-Assessment Program.
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!